I've a MongoDB query here which is running extremely slow without an index but the query fields are too big to index so i'm looking for advice on how to optimise this query or create valid index for it:
collection.aggregate([{
$match: {
article_id: {
$nin: read_article_ids
},
author_id: {
$in: liked_authors,
$nin: disliked_authors
},
word_count: {
$gte: 1000,
$lte: 10000
},
article_sentiment: {
$elemMatch: {
sentiments: mood
}
}
}
}, {
$sample: {
size: 4
}
}])
The collection in this case is a collection of articles with article_id, author_id, word_count, and article_sentiment. There is around 1.6 million documents in the collection and a query like this takes upwards of 10 seconds without an index. The box has 56gb of memory and is all around pretty specced out.
The query's function is to retrieve a batch of 4 articles by authors the user likes and that they've not read and that match a given sentiment (The article_sentiment key holds a nested array of key:value pairs)
So is this query incorrect for what i'm trying to achieve? Is there a way to improve it?
EDIT: Here is a sample document for this collection.
{
"_id": ObjectId("57f7dd597a1026d326fc02c4"),
"publication_name": "National News Inc",
"author_name": "John Hardwell",
"title": "How Shifting Policy Has Stunted Cultural Growth",
"article_id": "2f0896cd47c9423cb5a309c7277dd90d",
"author_id": "51b7f46f6c0f46f2949608c9ec2624d4",
"word_count": 1202,
"article_sentiment": [{
"sentiments": "happy",
"weight": 0.528596282005
}, {
"sentiments": "serious",
"weight": 0.569274544716
}, {
"sentiments": "relaxed",
"weight": 0.825395524502
}]
}
Related
I am beginner in MongoDB and struck at a place I am trying to fetch data from nested array but is it taking so long time as data is around 50K data, also it is not much accurate data, below is schema structure please see once -
{
"_id": {
"$oid": "6001df3312ac8b33c9d26b86"
},
"City": "Los Angeles",
"State":"California",
"Details": [
{
"Name": "Shawn",
"age": "55",
"Gender": "Male",
"profession": " A science teacher with STEM",
"inDate": "2021-01-15 23:12:17",
"Cars": [
"BMW","Ford","Opel"
],
"language": "English"
},
{
"Name": "Nicole",
"age": "21",
"Gender": "Female",
"profession": "Law student",
"inDate": "2021-01-16 13:45:00",
"Cars": [
"Opel"
],
"language": "English"
}
],
"date": "2021-01-16"
}
Here I am trying to filter date with date and Details.Cars like
db.getCollection('news').find({"Details.Cars":"BMW","date":"2021-01-16"}
it is returning details of other persons too which do not have cars- BMW , Only trying to display details of person like - Shawn which have BMW or special array value and date too not - Nicole, rest should not appear but is it not happening.
Any help is appreciated. :)
A combination of $match on the top-level fields and $filter on the array elements will do what you seek.
db.foo.aggregate([
{$match: {"date":"2021-01-16"}}
,{$addFields: {"Details": {$filter: {
input: "$Details",
as: "zz",
cond: { $in: ['BMW','$$zz.Cars'] }
}}
}}
,{$match: {$expr: { $gt:[{$size:"$Details"},0] } }}
]);
Notes:
$unwind is overly expensive for what is needed here and it likely means "reassembling" the data shape later.
We use $addFields where the new field to add (Details) already exists. This effectively means "overwrite in place" and is a common idiom when filtering an array.
The second $match will eliminate docs where the date matches but not a single entry in Details.Cars is a BMW i.e. the array has been filtered down to zero length. Sometimes you want to know this info so if this is the case, do not add the final $match.
I recommend you look into using real dates i.e. ISODate instead of strings so that you can easily take advantage of MongoDB date math and date formatting functions.
Is a common mistake think that find({nested.array:value}) will return only the nested object but actually, this query return the whole object which has a nested object with desired value.
The query is returning the whole document where value BMW exists in the array Details.Cars. So, Nicole is returned too.
To solve this problem:
To get multiple elements that match the criteria you can do an aggregation stage using $unwind to separate the different objects into array and match by the criteria you want.
db.collection.aggregate([
{
"$match": { "Details.Cars": "BMW", "date": "2021-01-26" }
},
{
"$unwind": "$Details"
},
{
"$match": { "Details.Cars": "BMW" }
}
])
This query first match by the criteria to avoid $unwind over all collection.
Then $unwind to get every document and $match again to get only the documents you want.
Example here
To get only one element (for example, if you match by _id and its unique) you can use $elemMatch in this way:
db.collection.find({
"Details.Cars": "BMW",
"date": "2021-01-16"
},
{
"Details": {
"$elemMatch": {
"Cars": "BMW"
}
}
})
Example here
You can use $elemenMatch into query or projection stage. Docs here and here
Using $elemMatch into query the way is this:
db.collection.find({
"Details": {
"$elemMatch": {
"Cars": "BMW"
}
},
"date": "2021-01-16"
},
{
"Details.$": 1
})
Example here
The result is the same. In the second case you are using positional operator to return, as docs says:
The first element that matches the query condition on the array.
That is, the first element where "Cars": "BMW".
You can choose the way you want.
Assuming we have a document in my MongoDB collection like the following:
{
"_id": "coffee",
"orders": [ "espresso", "cappuccino", "espresso", ... ],
}
How do I use a single update statement that ensures there are exactly say 2 espressos in this document, without knowing how many there are to begin with?
I know that using 2 consecutive statements I can do
db.test.update(
{ _id: "coffee" },
{ "$pull": { "orders": "espresso" } }
);
followed by
db.test.update(
{ "_id": "coffee" },
{ "$push": { "orders": { "$each": ["espresso", "espresso"] } } }
);
But when combining both into a single statement, MongoDB balks with an error 40, claiming Updating the path 'orders' would create a conflict at 'orders' (understandable enough - how does MongoDB what to do first?).
So, how can I do the above in a single statement? Please note that since I'll be using the above in the context of a larger unordered bulk operation, combining the above in an ordered bulk operation won't work.
Thanks for your help!
I have two models, one is user
userSchema = new Schema({
userID: String,
age: Number
});
and the other is the score recorded several times everyday for all users
ScoreSchema = new Schema({
userID: {type: String, ref: 'User'},
score: Number,
created_date = Date,
....
})
I would like to do some query/calculation on the score for some users meeting specific requirement, say I would like to calculate the average of score for all users greater than 20 day by day.
My thought is that firstly do the populate on Scores to populate user's ages and then do the aggregate after that.
Something like
Score.
populate('userID','age').
aggregate([
{$match: {'userID.age': {$gt: 20}}},
{$group: ...},
{$group: ...}
], function(err, data){});
Is it Ok to use populate before aggregate? Or I first find all the userID meeting the requirement and save them in a array and then use $in to match the score document?
No you cannot call .populate() before .aggregate(), and there is a very good reason why you cannot. But there are different approaches you can take.
The .populate() method works "client side" where the underlying code actually performs additional queries ( or more accurately an $in query ) to "lookup" the specified element(s) from the referenced collection.
In contrast .aggregate() is a "server side" operation, so you basically cannot manipulate content "client side", and then have that data available to the aggregation pipeline stages later. It all needs to be present in the collection you are operating on.
A better approach here is available with MongoDB 3.2 and later, via the $lookup aggregation pipeline operation. Also probably best to handle from the User collection in this case in order to narrow down the selection:
User.aggregate(
[
// Filter first
{ "$match": {
"age": { "$gt": 20 }
}},
// Then join
{ "$lookup": {
"from": "scores",
"localField": "userID",
"foriegnField": "userID",
"as": "score"
}},
// More stages
],
function(err,results) {
}
)
This is basically going to include a new field "score" within the User object as an "array" of items that matched on "lookup" to the other collection:
{
"userID": "abc",
"age": 21,
"score": [{
"userID": "abc",
"score": 42,
// other fields
}]
}
The result is always an array, as the general expected usage is a "left join" of a possible "one to many" relationship. If no result is matched then it is just an empty array.
To use the content, just work with an array in any way. For instance, you can use the $arrayElemAt operator in order to just get the single first element of the array in any future operations. And then you can just use the content like any normal embedded field:
{ "$project": {
"userID": 1,
"age": 1,
"score": { "$arrayElemAt": [ "$score", 0 ] }
}}
If you don't have MongoDB 3.2 available, then your other option to process a query limited by the relations of another collection is to first get the results from that collection and then use $in to filter on the second:
// Match the user collection
User.find({ "age": { "$gt": 20 } },function(err,users) {
// Get id list
userList = users.map(function(user) {
return user.userID;
});
Score.aggregate(
[
// use the id list to select items
{ "$match": {
"userId": { "$in": userList }
}},
// more stages
],
function(err,results) {
}
);
});
So by getting the list of valid users from the other collection to the client and then feeding that to the other collection in a query is the onyl way to get this to happen in earlier releases.
I'm trying to get MongoDB to aggregate for me over an array with different key-value pairs, without knowing keys (Just a simple sum would be ok.)
Example docs:
{data: [{a: 3}, {b: 7}]}
{data: [{a: 5}, {c: 12}, {f: 25}]}
{data: [{f: 1}]}
{data: []}
So basically each doc (or it's array really) can have 0 or many entries, and I don't know the keys for those objects, but I want to sum and average the values over those keys.
Right now I'm just loading a bunch of docs and doing it myself in Node, but I'd like to offload that work to MongoDB.
I know I can unwind those first, but how to proceed from there? How to sum/avg/min/max the values if I don't know the keys?
If you do not know the keys or cannot make a reasonable educated guess then you are basically stuck from going any further with the aggregation framework. You could supply "all of the keys" for consideration, but I supect your acutal data looks more like this:
{ "data": [{ "film": 10 }, { "televsion": 5 },{ "boardGames": 1 }] }
So there would be little point here findin out all the "key names" and then throwing that at an aggregation statement.
For the record though, "this is why you do not structure your data storage like this". Information like "film" here should not be used as a "key" name, because it is useful "data" that could be searched upon and most importantly "indexed" in a database system.
So your data should really look like this:
{
"data": [
{ "type": "film", "value": 10 },
{ "type": "televsion", "valule": 5 },
{ "type": "boardGames", "value": 1 }
]
}
Then the aggregation statement is simple, as are many other things:
db.collection.aggregate([
{ "$unwind": "$data" },
{ "$group": {
"_id": null,
"sum": { "$sum": "$data.value" },
"avg": { "$avg": "$data.value" }
}}
])
But since the key names are constantly changing in documents and do not have a uniform structure, then you need JavaScript processing on the server to traverse the keys, and that meand mapReduce:
db.collection.mapReduce(
function() {
this.data.forEach(function(data) {
Object.keys(data).forEach(function(key) {
emit(null,data[key]); // emit the value regardless of key name
});
});
},
function(key,values) {
return Array.sum(values); // Just summing for example
},
{ "out": { "inline": 1 } }
)
And of course the JavaScript execution here will work much more slowly than the native coded operators available to the aggregation framework.
So this should be an abject lesson as to why you don not use "data" as "key names" when storing data in a database. The aggregation framework works with standard structres and is fast, falling back to JavaScript processing is more flexible, but the cost is mostly in speed and other features.
I'm trying to sort this in MongoDB with mongojs on a find():
{
"songs": {
"bNppHOYIgRE": {
"id": "bNppHOYIgRE",
"title": "Kygo - ID (Ultra Music Festival Anthem)",
"votes": 1,
"added": 1428514707,
"guids": [
"MzM3NTUx"
]
},
"izJzdDPH9yw": {
"id": "izJzdDPH9yw",
"title": "Benjamin Francis Leftwich - Atlas Hands (Samuraii Edit)",
"votes": 1,
"added": 1428514740,
"guids": [
"MzM3NTUx"
]
},
"Yifz3X_i-F8": {
"id": "Yifz3X_i-F8",
"title": "M83 - Wait (Kygo Remix)",
"votes": 0,
"added": 1428494338,
"guids": []
},
"nDopn_p2wk4": {
"id": "nDopn_p2wk4",
"title": "Syn Cole - Miami 82 (Kygo Remix)",
"votes": 0,
"added": 1428494993,
"guids": []
}
}
}
and I want to sort the keys in the songs on votes ascending and added descending.
I have tried
db.collection(coll).find().sort({votes:1}, function(err, docs) {});
but that doesn't work.
If this is an operation that you're going to be doing often, I would strongly consider changing your schema. If you make songs an array instead of a map, then you can perform this query using aggregation.
db.coll.aggregate([{ "$unwind": "$songs" }, { "$sort": { "songs.votes": 1, "songs.added": -1 }}]);
And if you put each of these songs in a separate songs collection, then you could perform the query with a simple find() and sort().
db.songs.find().sort({ "votes": 1, "added": -1 });
With your current schema, however, all of this logic would need to be in your application and it would get messy. A possible solution would be to get all of the documents and while iterating through the cursor, for each document, iterate through the keys, adding them to an array. Once you have all of the subdocuments in the array, sorting the array according to votes and added.
It is possible, but unnecessarily complex. And, of course, you wouldn't be able to take advantage of indexes, which would have an impact on your performance.
You already include the key inside the subdocument, so I would really recommend you reconsider your schema.